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MLMarker: a machine learning framework for tissue inference and biomarker discovery.

Tine Claeys^1,2, Sam van Puyenbroeck^1,2, Kris Gevaert^1,2

¹VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium.

|June 25, 2026

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This summary is machine-generated.

MLMarker, a machine learning tool, infers tissue origins from proteomics data using a Random Forest model. It provides accurate tissue similarity scores and explanations, aiding in complex biological data interpretation.

Area of Science:

Proteomics
Machine Learning
Bioinformatics

Background:

Interpreting complex or sparse proteomics data is challenging.
Tissue origin inference is crucial for understanding disease mechanisms and biomarker discovery.

Purpose of the Study:

To develop and validate MLMarker, a machine learning tool for continuous tissue similarity scoring in proteomics.
To provide an interpretable framework for tissue inference and hypothesis generation from proteomics data.

Main Methods:

A Random Forest model was trained on proteomics data from 34 healthy tissues.
SHAP (SHapley Additive exPlanations) was used for protein-level explanations.
A penalty factor was incorporated to enhance robustness for samples with missing proteins.

Keywords:

AI Machine learning Mass spectrometry-based proteomics Public data reuse Tissue prediction

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Main Results:

MLMarker demonstrated high accuracy in a pan-cancer cohort.
The tool identified brain-like signatures in cerebral melanoma metastases.
It successfully inferred brain and pituitary origins in biofluid samples.

Conclusions:

MLMarker offers an interpretable and robust approach for tissue inference using proteomics data.
The tool facilitates hypothesis generation and aids in the interpretation of complex biological datasets.
MLMarker is available as a Python package and a Streamlit application for broader accessibility.